Meta-Analysis of the Precipitation Effect on Productivity of Eucalyptus Plantations in Brazil

Objective: To evaluate different regression models for estimating the potential productivity of eucalypt as a function of mean annual precipitation. Theoretical Framework: The research considers the influence of precipitation on forest productivity and the use of sigmoidal models as an alternative to represent growth and estimate timber production. Method: Mean annual precipitation data were obtained from INMET, while potential productivity data were sourced from Borges (2012). Three nonlinear (sigmoidal) models were fitted to estimate mean annual increment at 72 months of age as a function of precipitation. Model performance was assessed according to statistical criteria and biological consistency. Results and Discussion: A positive correlation was observed between precipitation and productivity (r ≈ 0.62). Richards and Logistic models showed the best performance, with the Logistic model standing out by presenting an asymptote of 67.2569 m3ha-1yr-1. Mean annual precipitation below 742 mm may significantly compromise the potential productivity of eucalyptus. Research Implications: The study highlights the influence and functional relationship of mean annual precipitation on the potential productivity of eucalypt, enabling volume estimation based on meteorological data from different regions of Brazil. Originality/Value: The study stands out for applying meta-regression with sigmoidal models to relate precipitation and eucalyptus productivity on a national scale, providing an innovative approach to predict climate impacts and support forest management strategies.